State-of-the-art Performance: We are not aware of any approach that can solve hundreds of different instances of non-convex trajectory optimization problems in parallel. Our work is a first such attempt in this direction. As we show later, the simpler alternative of running different instances on parallel CPU threads is not scalable to large batch sizes. We perform extensive simulations in Model Predictive Control (MPC) setting and show that our batch optimizer is crucial for navigating highly cluttered and dynamic environments with tens of moving obstacles. More concretely, our batch MPC dramatically improves the success rate and solution quality compared to the baseline approach that computes only a single locally optimal trajectory at each control loop. We also benchmark our batch optimizer against the state-of-the-art Cross-Entropy Method (CEM) [1]}, [2]} that can also search over different local minima. Our approach achieves a higher success rate of collision avoidance and better trajectory-tracking than CEM.
